CN116402305A - Tunnel construction progress dispatching command integrated management system - Google Patents

Tunnel construction progress dispatching command integrated management system Download PDF

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CN116402305A
CN116402305A CN202310399526.0A CN202310399526A CN116402305A CN 116402305 A CN116402305 A CN 116402305A CN 202310399526 A CN202310399526 A CN 202310399526A CN 116402305 A CN116402305 A CN 116402305A
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stone
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王珩
沈翔
裴小放
李亮
肖丽娜
楚跃峰
田路路
黄忍
尹守强
梁昊
杨立东
刘迪
吴竞一
杨晓徐
张阔
李凯旋
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China Tiesiju Civil Engineering Group Co Ltd CTCE Group
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Abstract

The invention relates to the technical field of tunnel construction, and particularly discloses a comprehensive management system for scheduling command of tunnel construction progress, which comprises the following components: the invention overcomes the defect of single analysis dimension in the prior art, improves the accuracy of analysis of the tunnel construction difficulty degree, thereby being beneficial to control the operation of the tunnel construction by related personnel, improving the working efficiency of the staff during tunnel excavation, and allocating related equipment and personnel aiming at the tunnel excavation difficulty and the tunnel inner wall maintenance difficulty.

Description

Tunnel construction progress dispatching command integrated management system
Technical Field
The invention relates to the technical field of tunnel construction, in particular to a comprehensive management system for scheduling command of tunnel construction progress.
Background
The current technology and society develop at a high speed, new possibility and development vigor are brought to tunnel construction, tunnel construction scale is also bigger and bigger, in the process of tunnel construction, because the excavation method is inconsistent with the tunnel excavation difficulty in the construction process, how to keep the tunnel construction progress to meet the current expectations becomes a big problem, if the tunnel construction progress is not guaranteed, the actual input use time of the tunnel is influenced, the time node of smooth traffic of the tunnel cannot be guaranteed, timeliness convenience service is difficult to bring to people, meanwhile, the use time of related mechanical equipment is prolonged, the burden of manpower resources and equipment resources is increased, and therefore, analysis and management on the difficulty degree of tunnel construction are necessary.
At present, the analysis management of the difficulty level of tunnel construction in the prior art has a series of defects, which are specifically embodied in the following layers: (1) In the prior art, the analysis of the tunnel construction difficulty degree often depends on the analysis of the images of the tunnel cut-off surface, the analysis of the related information of the excavation waste and the maintenance difficulty of the inner wall of the tunnel is lacked, the analysis dimension is single, and the accuracy of the analyzed tunnel construction difficulty degree is not high, so that the operation control of related personnel on the tunnel construction is affected to a certain extent, and the working efficiency of the staff during tunnel excavation is reduced.
(2) In the prior art, concrete analysis of relevant equipment and personnel for the difficulty of tunnel excavation and the difficulty of maintenance on the inner wall of a tunnel is lacking, relevant equipment and personnel are allocated for the excavation stage corresponding to the current time of the tunnel, the situation that the targeted analysis level is insufficient and the dimension to be considered is single exists, and the situation that the allocation quantity of relevant equipment and personnel is inaccurate exists, so that the construction period of tunnel completion is affected, the time for tunnel establishment is further shortened, the allocation quantity of relevant equipment and personnel is excessive, the loss of manpower and material resources is further caused, and the long-term sustainable development of tunnel excavation is not facilitated.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a tunnel construction progress scheduling command integrated management system, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme: a tunnel construction progress scheduling command integrated management system comprises: the tunnel construction detection module is used for detecting excavation waste corresponding to each excavation of the tunnel construction in a set period, further obtaining basic waste parameters corresponding to each excavation, and collecting images of tunnel sections of the tunnel construction in the set period, further obtaining tunnel section images corresponding to each test time point.
And the excavation construction difficulty analysis module is used for comprehensively analyzing a difficulty evaluation coefficient corresponding to tunnel construction based on the basic parameters of the waste corresponding to each excavation and the tunnel interception surface images corresponding to each test time point in the set period.
The tunnel inner wall detection module is used for detecting the tunnel inner wall, dividing the tunnel inner wall into inner wall subareas according to the equal area, and further obtaining characteristic parameters corresponding to the inner wall subareas, wherein the characteristic parameters comprise an outlet distance, a ground distance, a water content and a falling object volume corresponding to each detection time point.
The tunnel inner wall analysis module is used for analyzing maintenance difficulty coefficients and slump hazard coefficients corresponding to the inner wall subareas, and further comprehensively analyzing comprehensive maintenance difficulty coefficients corresponding to the tunnel inner walls.
And the allocation terminal is used for analyzing allocation parameters corresponding to the tunnel construction and the tunnel inner wall according to the difficulty evaluation coefficient corresponding to the tunnel construction and the comprehensive maintenance difficulty coefficient corresponding to the tunnel inner wall, wherein the allocation parameters comprise the number of allocation equipment and the number of allocation personnel.
The cloud database is used for storing the waste parameters corresponding to each normal excavation, storing the proper exploitation humidity range corresponding to the tunnel cut-off surface, storing the proper water content range of the tunnel inner wall, storing the volume of the allowed collapse objects, and storing the quantity of allocation equipment and the quantity of allocation personnel corresponding to each allocation index interval.
As a preferable scheme, the basic parameters of the waste material comprise the corresponding water content, temperature and hardness of each layout point.
As a preferable scheme, the method for analyzing the difficulty evaluation coefficient corresponding to the tunnel construction specifically comprises the following steps: and extracting waste parameters corresponding to each normal excavation from the cloud database, and further analyzing a water content mean value H corresponding to the normal excavation, a temperature mean value T corresponding to the normal excavation, a hardness mean value Y corresponding to the normal excavation and a total number M' of reference stones corresponding to the normal excavation.
Based on the water content H corresponding to each layout point of each excavation i ′ m Temperature T i ′ m And hardness Y i ′ m Analyzing the difference coefficient corresponding to the waste parameters of each excavation and the waste parameters of normal excavation
Figure BDA0004178916780000031
Where i is denoted as the number of each excavation, i=1, 2,.. 1 、λ 2 、λ 3 Respectively expressed as the corresponding difference duty factors of the preset water content, temperature and hardness.
Image acquisition is carried out on the waste materials excavated for each time, and then the volume V of each stone corresponding to each excavation is obtained ij Weighing each stone belonging to each excavation, thereby obtaining the weight G of each stone belonging to each excavation ij Where j is denoted as the number of each stone, j=1, 2.
Screening the maximum stone weight corresponding to each excavation according to the weight of each stone to which each excavation belongs
Figure BDA0004178916780000041
And minimum stone weight->
Figure BDA0004178916780000042
And weighing the waste excavated for each time, and further obtaining the weight G' of the waste corresponding to each time of excavation.
Analyzing weight deviation coefficient of stone corresponding to each excavation
Figure BDA0004178916780000043
Where e is expressed as a natural constant, k is expressed as the number of stones, G "is expressed as a preset allowable stone weight error, gamma 1 、γ 2 Respectively expressed as a preset weight coefficient corresponding to the adjacent stone weight deviation, the maximum stone weight and the minimum stone weight deviation.
Counting the total number SA of stones corresponding to each excavation i Further analyzing the digging difficulty coefficient corresponding to each digging
Figure BDA0004178916780000044
Wherein χ is 1 、χ 2 、χ 3 、χ 4 Respectively expressed as preset stone volume, stone weight deviation and excavation difficulty weight factors corresponding to the number of stones.
Comprehensive analysis of difficulty evaluation coefficients corresponding to tunnel construction
Figure BDA0004178916780000045
Where n is expressed as the total number of excavations, σ p The excavation difficulty coefficient is expressed as a tunnel cut-off surface to which the p-th test time point belongs, p is expressed as the number of each test time point, and p=1, 2.
As a preferable scheme, the specific analysis method of the mining difficulty coefficient corresponding to the tunnel cut-off surface to which each test time point belongs is as follows: acquiring the cross-sectional area S of each stone of the tunnel section corresponding to each test time point according to the tunnel section image corresponding to each test time point pr Wherein r is denoted as tunnelThe number of each stone to which the cut-off belongs, r=1, 2.
And acquiring the area S' corresponding to the tunnel section according to the tunnel section image corresponding to each test time point.
Screening the maximum stone cross-sectional area corresponding to the tunnel section at each test time point according to the cross-sectional area of each stone corresponding to the tunnel section at each test time point
Figure BDA0004178916780000051
And minimum stone cross-sectional area->
Figure BDA0004178916780000052
And counting the total number M 'of stones corresponding to the tunnel cut-off surface at each test time point' p 。
Analyzing stone excavation difficulty coefficients corresponding to tunnel cut-off sections to which each test time point belongs
Figure BDA0004178916780000053
Wherein t is expressed as the number of stones to which the tunnel section belongs, S 'is expressed as a preset stone reference cross-sectional area, S' is expressed as a preset allowed stone cross-sectional area error, delta 1 、δ 2 、δ 3 Respectively expressed as the preset stone cross-sectional area, the number of stones of the tunnel cut-off surface, and the influence scaling factor corresponding to the deviation of the stone cross-sectional area.
Acquiring the humidity SD corresponding to each humidity detection point of the corresponding cross section of each test time point pb Where b is denoted as the number of each humidity detection point, b=1, 2,..u.
Extracting a proper exploitation humidity range corresponding to the tunnel section from the cloud database, and further obtaining a proper exploitation humidity upper limit value SD corresponding to the tunnel section Upper part And a lower limit SD of humidity suitable for exploitation Lower part(s) Further analyzing the humidity fit coefficient of each humidity detection point corresponding to each test time point
Figure BDA0004178916780000061
And comparing the temperature suitability coefficient of each humidity detection point corresponding to each test time point with a preset humidity suitability coefficient threshold, and if the humidity suitability coefficient of a certain humidity detection point corresponding to a certain test time point is greater than or equal to the humidity suitability coefficient threshold, marking the humidity detection point as a humidity coincidence detection point, thereby obtaining each humidity coincidence detection point to which each test time point belongs.
Counting the quantity SJ of the humidity conforming to the detection points at each test time point p 。
According to the number SL of the humidity detection points of each test time point p Analyzing humidity level excavation difficulty coefficients corresponding to all test time points
Figure BDA0004178916780000062
Where u represents the number of preset wetness detecting points.
Comprehensively analyzing mining difficulty coefficients corresponding to tunnel cut-off surfaces of all test time points
Figure BDA0004178916780000063
Wherein alpha is 1 、α 2 And respectively representing the preset stone excavating difficulty and the scale factors corresponding to the humidity level excavating difficulty.
As a preferable scheme, the method for analyzing the maintenance difficulty coefficient corresponding to each inner wall subarea specifically comprises the following steps: and extracting the water content of each inner wall subarea corresponding to each detection time point from the characteristic parameters corresponding to each inner wall subarea.
And comparing the water content corresponding to each inner wall subarea at each detection time point with the range of the proper water content of the inner wall of the tunnel stored in the cloud database, and if the water content corresponding to a certain inner wall subarea at a certain detection time point is between the range of the proper water content of the inner wall of the tunnel, marking the detection time point as a normal time point, thereby obtaining each normal time point corresponding to each inner wall subarea.
Counting the number ZC of the normal time points of each inner wall subregion h And detecting the number of time points ZC h ' wherein h is denoted as the number of each inner wall sub-region, h=1, 2,..g.
According to the corresponding outlet distance CK of each inner wall subarea h Distance from ground DM h Analyzing maintenance difficulty coefficients corresponding to inner wall subareas
Figure BDA0004178916780000071
Where g is expressed as the number of inner wall subregions.
As a preferable scheme, the slump hazard coefficient corresponding to each tunnel inner wall subarea is analyzed, and the specific method is as follows: extracting slump volume VB corresponding to each tunnel inner wall subarea at each detection time point from characteristic parameters corresponding to each inner wall subarea hf Where f is denoted as the number of each test time point, f=1, 2.
Analyzing slump hazard coefficients corresponding to each inner wall subarea according to the volume VB' of the allowed slump stored in the cloud database
Figure BDA0004178916780000072
Where d is expressed as the number of detection times.
As a preferable solution, the comprehensive maintenance difficulty coefficient corresponding to the inner wall of the comprehensive analysis tunnel has a specific calculation formula as follows:
Figure BDA0004178916780000081
as a preferable scheme, the method for specifically analyzing the allocation parameters corresponding to the tunnel construction comprises the following steps: and importing the difficult evaluation coefficient corresponding to the tunnel construction into a functional relation diagram between a preset allocation index and the difficult evaluation coefficient, further obtaining the allocation index corresponding to the tunnel construction, further extracting the allocation equipment number and the allocation personnel number corresponding to each allocation index interval from the cloud database, and screening the allocation equipment number and the allocation personnel number corresponding to the tunnel construction according to the allocation index corresponding to the tunnel construction.
And similarly, analyzing the number of the allocation devices and the number of allocation personnel corresponding to the inner wall of the tunnel.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: (1) According to the invention, the waste materials in tunnel construction excavation are detected in the tunnel construction detection module, and the image acquisition is carried out on the cut-off surface of the tunnel, so that a foundation is laid for the analysis of the subsequent difficult assessment corresponding to the tunnel construction.
(2) According to the invention, in the excavation construction difficulty analysis module, not only is the image of the tunnel cut-off surface analyzed, but also the related information of the excavation waste is analyzed, so that the defect of single analysis dimension in the prior art is overcome, the accuracy of analysis of the tunnel construction difficulty degree is improved, the control of the operation of related personnel on tunnel construction is facilitated, and the working efficiency of the staff during tunnel excavation is improved.
(3) According to the invention, the tunnel inner wall is detected in the tunnel inner wall detection module, so that the characteristic parameters of the inner wall subareas are obtained, and powerful data support is provided for subsequent analysis of the tunnel inner wall.
(4) According to the invention, the maintenance difficulty and slump hazard of the inner wall subarea are analyzed in the tunnel inner wall analysis module, so that the comprehensive maintenance difficulty coefficient of the tunnel inner wall is analyzed, and a foundation is laid for subsequent allocation of related equipment and personnel.
(5) According to the invention, relevant equipment and personnel are allocated in the allocation terminal aiming at difficult tunnel excavation and difficult maintenance of the inner wall of the tunnel, so that the defects of insufficient targeted analysis level and single consideration dimension in the prior art are overcome, the accuracy of allocation quantity analysis of the relevant equipment and personnel is further improved, the construction period of tunnel completion is ensured, the loss of manpower and material resources is avoided, and the long-term sustainable development of tunnel excavation is facilitated.
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The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a schematic diagram of the module connection of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a comprehensive management system for scheduling and commanding tunnel construction progress, which comprises: the system comprises a tunnel construction detection module, an excavation construction difficulty analysis module, a tunnel inner wall detection module, a tunnel inner wall analysis module, a deployment terminal and a cloud database.
The tunnel construction detection module is connected with the excavation construction difficulty analysis module, the tunnel inner wall detection module is connected with the tunnel inner wall analysis module, the excavation construction difficulty analysis module and the tunnel inner wall analysis module are both connected with the allocation terminal, and the cloud database is respectively connected with the excavation construction difficulty analysis module and the tunnel inner wall analysis module.
The tunnel construction detection module is used for detecting excavation waste corresponding to each excavation of tunnel construction in a set period, further obtaining basic waste parameters corresponding to each excavation, and collecting images of tunnel cut-off surfaces of the tunnel construction in the set period, further obtaining tunnel cut-off surface images corresponding to each test time point.
The water content of the excavation waste is detected by using a water content tester, the temperature of the excavation waste is detected by using a temperature sensor, and the hardness of the excavation waste is detected by using a soil hardness tester.
According to the invention, the waste materials in tunnel construction excavation are detected in the tunnel construction detection module, and the image acquisition is carried out on the cut-off surface of the tunnel, so that a foundation is laid for the analysis of the subsequent difficult assessment corresponding to the tunnel construction.
The excavation construction difficulty analysis module is used for comprehensively analyzing a difficulty evaluation coefficient corresponding to tunnel construction based on basic parameters of wastes corresponding to each excavation and tunnel interception section images corresponding to each test time point in a set period.
In a specific embodiment of the present invention, the basic parameters of the waste material include the water content, temperature and hardness corresponding to each layout point.
In a specific embodiment of the present invention, the method for analyzing the difficulty evaluation coefficient corresponding to the tunnel construction specifically includes: and extracting waste parameters corresponding to each normal excavation from the cloud database, and further analyzing a water content mean value H corresponding to the normal excavation, a temperature mean value T corresponding to the normal excavation, a hardness mean value Y corresponding to the normal excavation and a total number M' of reference stones corresponding to the normal excavation.
The history excavation of each time that the equipment allocation and personnel allocation are not performed in the set history period is marked as each time of normal excavation, and each time of normal excavation is further obtained.
It should be noted that, according to the water content corresponding to each layout point to which each normal excavation belongs, the maximum water content and the minimum water content to which each normal excavation belongs are removed, the water content corresponding to all the rest layout points to which each normal excavation belongs is subjected to average processing, and then the average value of the water content corresponding to each normal excavation is obtained, and thus the average value is processed, and then the average value H of the water content corresponding to the normal excavation is obtained.
The temperature with the largest occurrence frequency is selected from the temperatures corresponding to the layout points of each normal excavation, and is further marked as the reference temperature corresponding to each normal excavation, and the reference temperature corresponding to each normal excavation is subjected to mean value processing, so that a temperature mean value T corresponding to the normal excavation is obtained.
The method is consistent with the analysis method of the water content average value corresponding to normal excavation, the hardness average value Y and the stone number average value corresponding to normal excavation are analyzed, and the stone number average value corresponding to normal excavation is used as the total number of reference stones corresponding to normal excavation.
Based on the water content H corresponding to each layout point of each excavation i ′ m Temperature T i ′ m And hardness Y i ′ m Analyzing the parameters of the waste materials belonging to each excavation and the waste materials belonging to normal excavationCoefficient of difference corresponding to material parameters
Figure BDA0004178916780000111
Where i is denoted as the number of each excavation, i=1, 2,.. 1 、λ 2 、λ 3 Respectively expressed as the corresponding difference duty factors of the preset water content, temperature and hardness.
Image acquisition is carried out on the waste materials excavated for each time, and then the volume V of each stone corresponding to each excavation is obtained ij Weighing each stone belonging to each excavation, thereby obtaining the weight G of each stone belonging to each excavation ij Where j is denoted as the number of each stone, j=1, 2.
Screening the maximum stone weight corresponding to each excavation according to the weight of each stone to which each excavation belongs
Figure BDA0004178916780000121
And minimum stone weight->
Figure BDA0004178916780000122
And weighing the waste excavated for each time, and further obtaining the weight G' of the waste corresponding to each time of excavation.
Analyzing weight deviation coefficient of stone corresponding to each excavation
Figure BDA0004178916780000123
Where e is expressed as a natural constant, k is expressed as the number of stones, G "is expressed as a preset allowable stone weight error, gamma 1 、γ 2 Respectively expressed as a preset weight coefficient corresponding to the adjacent stone weight deviation, the maximum stone weight and the minimum stone weight deviation.
Counting the total number SA of stones corresponding to each excavation i Further analyzing the digging difficulty coefficient corresponding to each digging
Figure BDA0004178916780000124
Wherein χ is 1 、χ 2 、χ 3 、χ 4 Respectively expressed as preset stone volume, stone weight deviation and excavation difficulty weight factors corresponding to the number of stones.
Comprehensive analysis of difficulty evaluation coefficients corresponding to tunnel construction
Figure BDA0004178916780000125
Where n is expressed as the total number of excavations, σ p The excavation difficulty coefficient is expressed as a tunnel cut-off surface to which the p-th test time point belongs, p is expressed as the number of each test time point, and p=1, 2.
In a specific embodiment of the present invention, the specific analysis method of the mining difficulty coefficient corresponding to the tunnel section to which each test time point belongs is as follows: acquiring the cross-sectional area S of each stone of the tunnel section corresponding to each test time point according to the tunnel section image corresponding to each test time point pr Where r is the number of each stone to which the tunnel section belongs, r=1, 2.
And acquiring the area S' corresponding to the tunnel section according to the tunnel section image corresponding to each test time point.
Screening the maximum stone cross-sectional area corresponding to the tunnel section at each test time point according to the cross-sectional area of each stone corresponding to the tunnel section at each test time point
Figure BDA0004178916780000131
And minimum stone cross-sectional area->
Figure BDA0004178916780000132
And counting the total number M 'of stones corresponding to the tunnel cut-off surface at each test time point' p 。
Analyzing stone excavation difficulty coefficients corresponding to tunnel cut-off sections to which each test time point belongs
Figure BDA0004178916780000133
Wherein t is expressed as the number of stones to which the tunnel section belongs, S' is expressed as a preset stone reference cross-sectional area,s' is expressed as a preset allowed stone cross-sectional area error, delta 1 、δ 2 、δ 3 Respectively expressed as the preset stone cross-sectional area, the number of stones of the tunnel cut-off surface, and the influence scaling factor corresponding to the deviation of the stone cross-sectional area.
Acquiring the humidity SD corresponding to each humidity detection point of the corresponding cross section of each test time point pb Where b is denoted as the number of each humidity detection point, b=1, 2,..u.
Extracting a proper exploitation humidity range corresponding to the tunnel section from the cloud database, and further obtaining a proper exploitation humidity upper limit value SD corresponding to the tunnel section Upper part And a lower limit SD of humidity suitable for exploitation Lower part(s) Further analyzing the humidity fit coefficient of each humidity detection point corresponding to each test time point
Figure BDA0004178916780000141
And comparing the temperature suitability coefficient of each humidity detection point corresponding to each test time point with a preset humidity suitability coefficient threshold, and if the humidity suitability coefficient of a certain humidity detection point corresponding to a certain test time point is greater than or equal to the humidity suitability coefficient threshold, marking the humidity detection point as a humidity coincidence detection point, thereby obtaining each humidity coincidence detection point to which each test time point belongs.
Counting the quantity SJ of the humidity conforming to the detection points at each test time point p 。
According to the number SL of the humidity detection points of each test time point p Analyzing humidity level excavation difficulty coefficients corresponding to all test time points
Figure BDA0004178916780000142
Where u represents the number of preset wetness detecting points.
Comprehensively analyzing mining difficulty coefficients corresponding to tunnel cut-off surfaces of all test time points
Figure BDA0004178916780000143
Wherein alpha is 1 、α 2 Respectively denoted as pre-emphasisAnd setting a scale factor corresponding to the stone excavation difficulty and the humidity level excavation difficulty.
According to the invention, in the excavation construction difficulty analysis module, not only is the image of the tunnel cut-off surface analyzed, but also the related information of the excavation waste is analyzed, so that the defect of single analysis dimension in the prior art is overcome, the accuracy of analysis of the tunnel construction difficulty degree is improved, the control of the operation of related personnel on tunnel construction is facilitated, and the working efficiency of the staff during tunnel excavation is improved.
The tunnel inner wall detection module is used for detecting the tunnel inner wall, dividing the tunnel inner wall into inner wall subareas according to the equal area, and further obtaining characteristic parameters corresponding to the inner wall subareas, wherein the characteristic parameters comprise an outlet distance, a ground distance, a water content and a falling object volume corresponding to each detection time point.
The method is characterized in that a water content tester is used for testing the water content of the inner wall of the tunnel, and a camera is used for detecting images of the tunnel, so that characteristic parameters corresponding to the subareas of the inner wall of each tunnel are obtained.
The outlet distance corresponding to each inner wall sub-region is the shortest distance between the center point of each inner wall sub-region and the tunnel portal, and the ground distance corresponding to each inner wall sub-region is the perpendicular distance between the center point of each inner wall sub-region and the ground.
According to the invention, the tunnel inner wall is detected in the tunnel inner wall detection module, so that the characteristic parameters of the inner wall subareas are obtained, and powerful data support is provided for subsequent analysis of the tunnel inner wall.
The tunnel inner wall analysis module is used for analyzing maintenance difficulty coefficients and slump hazard coefficients corresponding to the inner wall subareas, and further comprehensively analyzing comprehensive maintenance difficulty coefficients corresponding to the tunnel inner walls.
In a specific embodiment of the present invention, the method for analyzing the maintenance difficulty coefficient corresponding to each inner wall sub-region specifically includes: and extracting the water content of each inner wall subarea corresponding to each detection time point from the characteristic parameters corresponding to each inner wall subarea.
And comparing the water content corresponding to each inner wall subarea at each detection time point with the range of the proper water content of the inner wall of the tunnel stored in the cloud database, and if the water content corresponding to a certain inner wall subarea at a certain detection time point is between the range of the proper water content of the inner wall of the tunnel, marking the detection time point as a normal time point, thereby obtaining each normal time point corresponding to each inner wall subarea.
Counting the number ZC of the normal time points of each inner wall subregion h And detecting the number of time points ZC h ' wherein h is denoted as the number of each inner wall sub-region, h=1, 2,..g.
According to the corresponding outlet distance CK of each inner wall subarea h Distance from ground DM h Analyzing maintenance difficulty coefficients corresponding to inner wall subareas
Figure BDA0004178916780000161
Where g is expressed as the number of inner wall subregions.
In a specific embodiment of the invention, slump hazard coefficients corresponding to the inner wall subareas of each tunnel are analyzed, and the specific method comprises the following steps: extracting slump volume VB corresponding to each tunnel inner wall subarea at each detection time point from characteristic parameters corresponding to each inner wall subarea hf Where f is denoted as the number of each test time point, f=1, 2.
Analyzing slump hazard coefficients corresponding to each inner wall subarea according to the volume VB' of the allowed slump stored in the cloud database
Figure BDA0004178916780000162
Where d is expressed as the number of detection times.
In a specific embodiment of the present invention, the comprehensive maintenance difficulty coefficient corresponding to the inner wall of the comprehensive analysis tunnel has a specific calculation formula as follows:
Figure BDA0004178916780000171
according to the invention, the maintenance difficulty and slump hazard of the inner wall subarea are analyzed in the tunnel inner wall analysis module, so that the comprehensive maintenance difficulty coefficient of the tunnel inner wall is analyzed, and a foundation is laid for subsequent allocation of related equipment and personnel.
The allocation terminal is used for analyzing allocation parameters corresponding to the tunnel construction and the tunnel inner wall according to the difficulty evaluation coefficient corresponding to the tunnel construction and the comprehensive maintenance difficulty coefficient corresponding to the tunnel inner wall, wherein the allocation parameters comprise the number of allocation equipment and the number of allocation personnel.
In a specific embodiment of the present invention, the allocation parameters corresponding to the tunnel construction are specifically analyzed by: and importing the difficult evaluation coefficient corresponding to the tunnel construction into a functional relation diagram between a preset allocation index and the difficult evaluation coefficient, further obtaining the allocation index corresponding to the tunnel construction, further extracting the allocation equipment number and the allocation personnel number corresponding to each allocation index interval from the cloud database, and screening the allocation equipment number and the allocation personnel number corresponding to the tunnel construction according to the allocation index corresponding to the tunnel construction.
It should be noted that, in a preset functional relationship diagram between the blending index and the difficulty evaluation coefficient, where the x-axis is the difficulty evaluation coefficient, the y-axis is the blending index, and the blending index increases with the increase of the difficulty evaluation coefficient, and the increasing amplitude is larger, in one embodiment, if the functional relationship between the blending index and the difficulty evaluation coefficient is y=f (x), then x is randomly selected on the functional relationship diagram between the blending index and the difficulty evaluation coefficient 2 And x 1 And x is 2 ＞＞x 1 Then
Figure BDA0004178916780000172
And similarly, analyzing the number of the allocation devices and the number of allocation personnel corresponding to the inner wall of the tunnel.
According to the invention, relevant equipment and personnel are allocated in the allocation terminal aiming at difficult tunnel excavation and difficult maintenance of the inner wall of the tunnel, so that the defects of insufficient targeted analysis level and single consideration dimension in the prior art are overcome, the accuracy of allocation quantity analysis of the relevant equipment and personnel is further improved, the construction period of tunnel completion is ensured, the loss of manpower and material resources is avoided, and the long-term sustainable development of tunnel excavation is facilitated.
The cloud database is used for storing waste parameters corresponding to each normal excavation, storing a proper exploitation humidity range corresponding to a tunnel cut-off surface, storing a proper water content range of the inner wall of the tunnel, storing the volume of the allowed collapse objects, and storing the quantity of allocation equipment and the quantity of allocation personnel corresponding to each allocation index interval.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art of describing particular embodiments without departing from the structures of the invention or exceeding the scope of the invention as defined by the claims.

Claims (8)

1. The utility model provides a tunnel construction progress dispatch commander integrated management system which characterized in that includes:
the tunnel construction detection module is used for detecting excavation waste corresponding to each excavation of the tunnel construction in a set period, further obtaining basic waste parameters corresponding to each excavation, and collecting images of tunnel sections of the tunnel construction in the set period, further obtaining tunnel section images corresponding to each test time point;
the excavation construction difficulty analysis module is used for comprehensively analyzing a difficulty evaluation coefficient corresponding to tunnel construction based on basic parameters of wastes corresponding to each excavation and tunnel interception face images corresponding to each test time point in a set period;
the tunnel inner wall detection module is used for detecting the tunnel inner wall, dividing the tunnel inner wall into inner wall subareas according to the equal area, and further obtaining characteristic parameters corresponding to the inner wall subareas, wherein the characteristic parameters comprise an outlet distance, a ground distance, water content and slump volume corresponding to each detection time point;
the tunnel inner wall analysis module is used for analyzing maintenance difficulty coefficients and slump hazard coefficients corresponding to the inner wall subareas, and further comprehensively analyzing comprehensive maintenance difficulty coefficients corresponding to the tunnel inner walls;
the allocation terminal is used for analyzing allocation parameters corresponding to the tunnel construction and the tunnel inner wall according to the difficulty evaluation coefficient corresponding to the tunnel construction and the comprehensive maintenance difficulty coefficient corresponding to the tunnel inner wall, wherein the allocation parameters comprise the number of allocation equipment and the number of allocation personnel;
the cloud database is used for storing the waste parameters corresponding to each normal excavation, storing the proper exploitation humidity range corresponding to the tunnel cut-off surface, storing the proper water content range of the tunnel inner wall, storing the volume of the allowed collapse objects, and storing the quantity of allocation equipment and the quantity of allocation personnel corresponding to each allocation index interval.
2. The tunnel construction progress scheduling command integrated management system according to claim 1, wherein: the basic parameters of the waste material comprise the water content, the temperature and the hardness corresponding to each layout point.
3. The tunnel construction progress scheduling command integrated management system according to claim 2, wherein: the method for analyzing the difficulty evaluation coefficient corresponding to the tunnel construction comprises the following specific steps:
extracting waste parameters corresponding to each normal excavation from the cloud database, and further analyzing a water content average value H corresponding to the normal excavation, a temperature average value T corresponding to the normal excavation, a hardness average value Y corresponding to the normal excavation and a total number M' of reference stones corresponding to the normal excavation;
based on the water content H corresponding to each layout point of each excavation i ′ m Temperature T i ′ m And hardness Y i ′ m Analyzing the difference coefficient corresponding to the waste parameters of each excavation and the waste parameters of normal excavation
Figure FDA0004178916770000021
Where i is denoted as the number of each excavation, i=1, 2,.. 1 、λ 2 、λ 3 Respectively expressed as preset water content, temperature and hardnessThe difference duty factor;
image acquisition is carried out on the waste materials excavated for each time, and then the volume V of each stone corresponding to each excavation is obtained ij Weighing each stone belonging to each excavation, thereby obtaining the weight G of each stone belonging to each excavation ij Where j is the number of each stone, j=1, 2,..k;
screening the maximum stone weight corresponding to each excavation according to the weight of each stone to which each excavation belongs
Figure FDA0004178916770000022
And minimum stone weight
Figure FDA0004178916770000023
Weighing the waste excavated for each time to obtain the weight G' of the waste corresponding to each time;
analyzing weight deviation coefficient of stone corresponding to each excavation
Figure FDA0004178916770000031
Where e is expressed as a natural constant, k is expressed as the number of stones, G "is expressed as a preset allowable stone weight error, gamma 1 、γ 2 Respectively representing the preset weight coefficients corresponding to the weight deviation of the adjacent stones, the maximum stone weight and the minimum stone weight;
counting the total number SA of stones corresponding to each excavation i Further analyzing the digging difficulty coefficient corresponding to each digging
Figure FDA0004178916770000032
Wherein χ is 1 、χ 2 、χ 3 、χ 4 Respectively representing the preset stone volume, the stone weight deviation and the excavation difficulty weight factors corresponding to the stone quantity;
comprehensive analysis of difficulty evaluation coefficients corresponding to tunnel construction
Figure FDA0004178916770000033
Where n is expressed as the total number of excavations, σ p The excavation difficulty coefficient is expressed as a tunnel cut-off surface to which the p-th test time point belongs, p is expressed as the number of each test time point, and p=1, 2.
4. The tunnel construction progress scheduling command integrated management system according to claim 3, wherein: the specific analysis method of the mining difficulty coefficient corresponding to the tunnel cut-off surface to which each test time point belongs comprises the following steps:
acquiring the cross-sectional area S of each stone of the tunnel section corresponding to each test time point according to the tunnel section image corresponding to each test time point pr Wherein r is the number of each stone to which the tunnel section belongs, r=1, 2,;
acquiring the area S' corresponding to the tunnel section according to the tunnel section image corresponding to each test time point;
screening the maximum stone cross-sectional area corresponding to the tunnel section at each test time point according to the cross-sectional area of each stone corresponding to the tunnel section at each test time point
Figure FDA0004178916770000041
And minimum stone cross-sectional area->
Figure FDA0004178916770000042
And counting the total number M 'of stones corresponding to the tunnel cut-off surface at each test time point' p ；
Analyzing stone excavation difficulty coefficients corresponding to tunnel cut-off sections to which each test time point belongs
Figure FDA0004178916770000043
Wherein t is expressed as the number of stones to which the tunnel section belongs, S 'is expressed as a preset stone reference cross-sectional area, S' is expressed as a preset allowed stone cross-sectional area error, delta 1 、δ 2 、δ 3 Respectively expressed as the preset stone cross-sectional area and tunnel sectionThe influence proportion coefficient corresponding to the stone quantity and the stone cross-sectional area deviation;
acquiring the humidity SD corresponding to each humidity detection point of the corresponding cross section of each test time point pb Wherein b is denoted as the number of each wetness detection point, b=1, 2, u;
extracting a proper exploitation humidity range corresponding to the tunnel section from the cloud database, and further obtaining a proper exploitation humidity upper limit value SD corresponding to the tunnel section Upper part And a lower limit SD of humidity suitable for exploitation Lower part(s) Further analyzing the humidity fit coefficient of each humidity detection point corresponding to each test time point
Figure FDA0004178916770000051
Comparing the temperature suitability coefficient of each humidity detection point corresponding to each test time point with a preset humidity suitability coefficient threshold value, and if the humidity suitability coefficient of a certain humidity detection point corresponding to a certain test time point is greater than or equal to the humidity suitability coefficient threshold value, marking the humidity detection point as a humidity coincidence detection point, thereby obtaining each humidity coincidence detection point to which each test time point belongs;
counting the quantity SJ of the humidity conforming to the detection points at each test time point p ；
According to the number SL of the humidity detection points of each test time point p Analyzing humidity level excavation difficulty coefficients corresponding to all test time points
Figure FDA0004178916770000052
Wherein u represents the number of preset humidity detection points;
comprehensively analyzing mining difficulty coefficients corresponding to tunnel cut-off surfaces of all test time points
Figure FDA0004178916770000053
Wherein alpha is 1 、α 2 And respectively representing the preset stone excavating difficulty and the scale factors corresponding to the humidity level excavating difficulty.
5. The tunnel construction progress scheduling command integrated management system according to claim 1, wherein: the specific method for analyzing the maintenance difficulty coefficient corresponding to each inner wall subarea comprises the following steps:
extracting the water content of each inner wall subarea corresponding to each detection time point from the characteristic parameters corresponding to each inner wall subarea;
comparing the water content of each inner wall subarea at each detection time point with the range of the proper water content of the inner wall of the tunnel stored in the cloud database, and if the water content of a certain inner wall subarea at a certain detection time point is between the range of the proper water content of the inner wall of the tunnel, marking the detection time point as a normal time point, thereby obtaining each normal time point corresponding to each inner wall subarea;
counting the number ZC of the normal time points of each inner wall subregion h And detecting the number of time points ZC h ' wherein h is the number of each inner wall sub-region, h=1, 2, g;
according to the corresponding outlet distance CK of each inner wall subarea h Distance from ground DM h Analyzing maintenance difficulty coefficients corresponding to inner wall subareas
Figure FDA0004178916770000061
Where g is expressed as the number of inner wall subregions.
6. The integrated management system for scheduling and commanding of tunnel construction progress according to claim 5, wherein: the slump hazard coefficient corresponding to each tunnel inner wall subarea is analyzed, and the concrete method comprises the following steps:
extracting slump volume VB corresponding to each tunnel inner wall subarea at each detection time point from characteristic parameters corresponding to each inner wall subarea hf Where f is expressed as the number of each test time point, f=1, 2, d;
analyzing slump hazard coefficients corresponding to each inner wall subarea according to the volume VB' of the allowed slump stored in the cloud database
Figure FDA0004178916770000062
Where d is expressed as the number of detection times.
7. The tunnel construction progress scheduling command integrated management system according to claim 6, wherein: the comprehensive maintenance difficulty coefficient corresponding to the comprehensive analysis tunnel inner wall is calculated according to the following specific formula:
Figure FDA0004178916770000071
8. the tunnel construction progress scheduling command integrated management system according to claim 1, wherein: the specific analysis method of the allocation parameters corresponding to the tunnel construction comprises the following steps:
leading the difficult evaluation coefficient corresponding to the tunnel construction into a function relation diagram between a preset allocation index and the difficult evaluation coefficient, further obtaining an allocation index corresponding to the tunnel construction, further extracting the allocation equipment number and the allocation personnel number corresponding to each allocation index interval from a cloud database, and screening the allocation equipment number and the allocation personnel number corresponding to the tunnel construction according to the allocation index corresponding to the tunnel construction;
and similarly, analyzing the number of the allocation devices and the number of allocation personnel corresponding to the inner wall of the tunnel.
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